Data science teams are an integral part of early-stage or growth-stage start-ups as midlevel and enterprise companies. A data science team can include a wide range of roles that take care of the end-to-end machine learning lifecycle from project conceptualization to execution, delivery, and monitoring:
The manager of a data science team in an enterprise organization has multiple responsibilities, including the following:
As the data science manager, it’s critical to have a structured, efficient hiring process, especially in a highly competitive job market where the demand outstrips the supply of data science and machine learning talent. A transparent, thoughtful, and open hiring process sends a strong signal to prospective candidates about the intent and culture of both the data science team and the company, and can make your company a stronger choice when the candidates are selecting an offer.
In this blog, you’ll learn about key aspects of the process of hiring a top-class data science team. You’ll dive into the process of recruitment, interviewing, and evaluating candidates to learn how to find the ones who can help your business improve its data science capabilities.
Benefits of an Efficient Hiring Process
Recent events have accelerated organizations’ focus on digital and AI transformation, resulting in a very tight labor market when you’re looking for data sciencedigital skills, like machinelike data science and machine learning, statistics, and programming.
A structured, efficient hiring process enables teams to move faster, make better decisions, and ensure a good experience for the candidates. Even if candidates don’t get an offer, a positive experience interacting with the data science and the recruitment teams makes them more likely to share good feedback on platforms like Glassdoor, which might encourage others to interview at the company.
Hiring Data Science Teams
A good hiring process is a multistep process, and in this section, you’ll look at every step of the process in detail.
Building a Funnel for Talent
Depending on the size of the data science team, the hiring manager may have to assume the responsibility of reaching out to candidates and building a pipeline of talent. In larger organizations, managers can work with in-house recruiters or even third-party recruitment agencies to source talent.
It’s important for the data science managers to clearly convey the requirements for the recruited candidates, such as the number of candidates desired and the profiles of those candidates. Candidate profiles might include things like previous experience, education or certifications, skill set or tech stack, and experience with specific use cases. Using these details, recruiters can then start their marketing, advertising, and outreach campaigns on platforms, like LinkedIn, Glassdoor, Twitter, HackerRank, and LeetCode.
In several cases, recruiters may identify candidates who are a strong fit but who may not be on the job market or are not actively looking for new roles. A database of all such candidates ought to be maintained so that recruiters can proactively reach out to them at a more suitable time and reengage the candidates.
Another trusted source of identifying good candidates is through employee referrals. An in-house employee referral program that incentivizes current employees to refer candidates from their network is often an effective way to attract the specific types of talent you’re looking for.
The data science leader should also publicize their team’s work through channels, like conferences or workshops, company blogs, podcasts, media, and social media. By investing dedicated time and energy in building up the profile of the data science team, it’s more likely that candidates will reach out to your company seeking data science opportunities.
When looking for a diverse set of talent, the search an be difficult as data science is a male dominated field. As a result, traditional recruiting paths will continue to reflect this bias. Reaching out and building relationships with groups such as Women in Data Science, can help broad the pipeline of talent you attract.
Defining Roles and Responsibilities
Good candidates are more likely to apply for roles that have a clear job description, including a list of potential data science use cases, a list of required skills and tech stack, and a summary of the day-to-day work, as well as insights into the interviewing process and time lines. Crafting specific, accurate job descriptions is a critical—if often overlooked—aspect of attracting candidates. The more information and clarity you provide up front, the more likely it is that candidates have sufficient information to decide if it’s a suitable role for them and if they should go ahead with the application or not. If you’re struggling with creating this, you can start with an existing job description template and then customize it in accordance with the needs of the team and company.
It's also critical to not over populate a job description with every possible skill or experience you hope a candidate brings. That will narrow your potential applicant pool. Instead focus on those skills and experiences that are absolutely critical. The right candidate will be able to pick up other skills on the job.
It can be useful for the job description to include links to any recent publications, blogs, or interviews by members of the data science team. These links provide additional details about the type of work your team does and also offer candidates a glimpse of other team members.
Here are some job description templates for the different roles in a data science team:
When compared to software engineering interviews, the interview process for data science roles is still very unstructured, and data science candidates are often uncertain about what the interview process involves. The professional position of data scientist has only existed for a little over a decade, and in that time, the role has evolved and transformed, resulting in even newer, more specialized roles, such as data engineer, machine learning engineer, applied scientist, research scientist, and product data scientist.
Because of the diversity of roles that could be considered data science, it’s important for a data science manager to customize the interviewing process depending on the specific profile they’re seeking. Data scientists need to have expertise in multiple domains, and one or more second-round interviews can be tailored around these core skills:
Given how tight the job market is for data science talent, it’s important to not over complicate the process. The more steps in the process, the longer it will take and the higher the likelihood you will lose viable candidates to other offers. So be thoughtful in your approach and evaluate it periodically to align with the market.
Types of Data Science Interviews
Interviews are often a multistep process and can involve multiple steps of assessments.
To save time, one or more screening rounds can be conducted before inviting candidates for second-round interviews. These screening interviews can take place virtually and involve an assessment of essential skills, like programming and machine learning, along with a deep dive into the candidate’s experience, projects, career trajectory, and motivation to join the company. These screening rounds can be conducted by the data science team itself or outsourced to other companies, like HackerRank, HackerEarth, Triplebyte, or Karat.
Once candidates have passed the screening interviews, the top candidates will be invited to a second interview, either virtually or in person. The data science manager has to take the lead in terms of coordinating with internal interviewers to confirm the schedule for the series of interviews that will assess the candidate’s skills, as described earlier. On the day of the second-round interviews, the hiring manager needs to help the candidate feel welcome and explain how the day will proceed. Some companies like to invite candidates to lunch with other team members, which breaks the ice by allowing the candidate to interact with potential team members in a social setting.
Each interview in the series should start by having the interviewer introduce themself and provide a brief summary of the kind of work they do. Depending on the types of interviews and assessments the candidate has already been through, the rest of the interview could focus on the core skill set to be evaluated or other critical considerations. Wherever possible, interviewers should offer the candidate hints if they get stuck and otherwise try to make them feel comfortable with the process. The last five to ten minutes of each interview should be reserved for the candidate to ask questions to the interviewer. This is a critical component of second-round interviews, as the types of questions a candidate asks offer a great deal of information about how carefully they’ve considered the role.
Before the candidate leaves, it’s important for the recruiter and hiring manager to touch base with the candidate again, inquire about their interview experience, and share time lines for the final decision.
It is common for there to be some sort of case study or technical assessment to get a better understanding of a candidate’s approach to problem solving, dealing with ambiguity and practical skills. This provides the company with good information about how the candidate may perform in the role It also is an opportunity to show the candidate what type of data and problems they may work on when working for you.
After the second-round interviews and technical assessment, the hiring manager needs to coordinate a debrief session. In this meeting, every interviewer shares their views based on their experience with the candidate and offers a recommendation if the candidate should be hired or not.
After obtaining the feedback from each member of the interview panel, the hiring manager also shares their opinion. If the candidate unanimously receives a strong hire or a strong no-hire signal, then the hiring manager’s decision is simple.
However, there may be candidates who perform well in some interviews but not so well in others, and who elicit mixed feedback from the interview panel. In cases like this, the hiring manager has to make a judgment call on whether that particular candidate should be hired or not. In some cases, an offer may be extended if a candidate didn’t do well in one or more interviews but the panel is confident that the candidate can learn and upskill on the job, and is a good fit for the team and the company.
If multiple candidates have interviewed for the same role, then a relative assessment of the different candidates should be considered, and the strongest candidate or candidates, depending on the number of roles to be filled, should be considered.
While most of the interviews focus on technical data science skills, it’s also important for interviewers to use their time with the candidate to assess soft skills, like communication, clarity of thought, problem-solving ability, business sense, and leadership values. Many large companies place a very strong emphasis on behavioral interviews, and poor performance in this interview can lead to a rejection, even if the candidate did well on the technical assessments.
After the debrief session, the data science manager needs to make their final decision and share the outcome, along with a compensation budget, with the recruiter. If there’s no recruiter involved, the manager can move directly to making the candidate an offer.
It’s important to move quickly when it comes to making and conveying the decision, especially if candidates are interviewing at multiple companies. Being fast and flexible in the hiring process gives companies an edge that candidates appreciate and take into consideration in their decision-making process.
Once the offer and details of compensation have been sent to the candidate, it’s essential to close the offer quickly to prevent candidates from using your offer as leverage at other companies. Including a deadline for the offer can sometimes work to the company’s advantage by incentivizing candidates to make their decision faster. If negotiations stretch and the candidate seems to lose interest in the process, the hiring manager should assess whether the candidate is really motivated to be part of the team. Sometimes, it may move things along if the hiring manager steps in and has another brief call with the candidate to help remove any doubts about the type of work and projects. However, additional pressure on the candidates can often work to your disadvantage and may put off a skilled and motivated candidate in whom the company has already invested a lot of time and money.
In this article, you’ve looked at an overview of the process of hiring a data science team, including the roles and skills you might be hiring for, the interview process, and how to evaluate and make decisions about candidates. In a highly competitive data science job market, having a robust pipeline of talent, and a fast, fair, and structured hiring process can give companies a competitive edge.
Published by Domino Data Lab
Reproducibility is a cornerstone of the scientific method and ensures that tests and experiments can be reproduced by different teams using the same method. In the context of data science, reproducibility means that everything needed to recreate the model and its results such as data, tools, libraries, frameworks, programming languages and operating systems, have been captured, so with little effort the identical results are produced regardless of how much time has passed since the original project.
Reproducibility is critical for many aspects of data science including regulatory compliance, auditing, and validation. It also helps data science teams be more productive, collaborate better with nontechnical stakeholders, and promote transparency and trust in machine learning products and services.
In this article, you’ll learn about the benefits of reproducible data science and how to ingrain reproducibility in every data science project. You’ll also learn how to cultivate an organizational culture that promotes greater reproducibility, accountability, and scalability.
What does it mean to be reproducible?
Machine learning systems are complex, incorporating code, data sets, models, hyperparameters, pipelines, third-party packages, model training and development configurations across machines, operating systems, and environments. To put it simply, reproducing a data science experiment is difficult if not impossible if you can’t recreate the exact same conditions used to build the model. To do that, all artifacts have to be captured and versioned in an accessible repository. That way when a model needs to be reproduced, the exact environment, using the exact training data and code, within the exact package combination can be recreated easily. Too often it's an archeological expedition that can take weeks or months (or potentially never) when the artifacts are not captured at the time of creation.
While the focus on reproducibility is a phenomenon in data science, it has been a cornerstone of scientific research across all kinds of industries, including clinical and life sciences, healthcare, and finance. If your company is unable to produce consistent experimental results, that can significantly impact your productivity, waste valuable resources, and impair decision-making.
Situations Where Reproducibility Matters
In data science, reproducibility is especially vital for data scientists to apply the experimental findings to their own work.
In highly regulated industries like insurance, finance and life sciences, all aspects of a model have to be documented and captured to provide full transparency, justification and validation on how models are developed and used inside an organization. This includes the type of algorithm being used, why the algorithm has been selected and how the model has been implemented within the business. A big part of complying involves being able to exactly reproduce the results of a model at any time. Without a system for capturing the artifacts, code, data, environment, packages and tools used to build a model this can be a time consuming, difficult task.
In all industries models should be validated prior to deployment to ensure the results are repeatable, understood and the model will achieve its intended purpose. Too often this is a time intensive process with validation teams having to piece together the environment, tools, data and other artifacts that were used to create the model, which slows down moving a model into production. When an organization is able to reproduce a model instantly, validators can focus on their core function of ensuring the model is robust and accurate.
Data science innovation happens when teams are able to collaborate and compound knowledge. It doesn’t happen when they have to spend time painstakingly recreating a prior experiment or accidentally duplicate work. When all work is easily reproducible, and easily searched, it's easy to build on prior work to innovate. It also means that as team staffing changes, institutional knowledge doesn’t disappear.
Ingraining Reproducibility in Data Science Projects
Instilling a culture of reproducibility in data science across an organization requires a long-term strategy, technology investment, and buy-in from data and engineering leadership. In this section, you’ll learn about a few established best practices for conducting and promoting reproducible data science work in your industry.
Version control refers to the process of tracking and managing changes to artifacts, like code, data, labels, models, hyperparameters, experiments, dependencies, documentation, as well as environments for training and inference.
The building blocks of version control for data science are more complex than software projects, making reproducibility that much more difficult and challenging. For code, there are multiple platforms, like GitHub, GitLab, and Bitbucket, that can be used to store, update, and track code, like Python scripts, Jupyter Notebooks, and configuration files, in common repositories.
However that isn’t sufficient. Datasets need to be captured and versioned as well. So do the environments, tools and packages. This is because code may or may not run the same on a different version of Python or R, for example. Data may have changed even if pulled with the same parameters. Similarly capturing different versions of models and corresponding hyperparameters for each experiment is important to reproduce and replicate the results of a winning model that might be deployed to production.
Reproducing end-to-end data science experiments is a complex, technical challenge that can be achieved much more efficiently using platforms like Domino’s Enterprise MLOps platform which eliminates all manual work and ensures reproducibility at scale.
Building accurate and reproducible data science models requires robust and scalable infrastructure for data storage and warehousing, data pipelines, feature stores, model stores, deployment pipelines, and experiment tracking. For machine learning models that serve predictions in real time, the importance of reproducibility is even higher in order to quickly resolve bugs and performance issues.
End-to-end machine learning pipelines involve multiple components, and an organizational strategy for reproducible data science work must carefully plan for the tooling and infrastructure to enable it. Engineering reproducible workflows requires sophisticated tooling to encompass code, data, models, dependencies, experiments, pipelines, and runtime environments.
For many organizations, it makes sense to buy (vs. build) such scalable workflows focused on reproducible data science.
Reproducible research is a cornerstone of scientific research. Reproducibility is especially significant for cross-functional disciplines like data science that involve multiple artifacts, like code, data, models, and hyperparameters, as well as a diverse set of practitioners and stakeholders. Reproducing complex experiments and results is, therefore, essential for teams and organizations when making important decisions like which models to deploy, identifying root causes when the models break down, and building trust in data science work.
Reproducing data science results requires a complex set of processes and infrastructure that is not easy or necessary for many teams and companies to build in-house.
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